Peatlands represent 16-33% of the global soil carbon pool. Temperate peatlands typically dominated by grasses and sedges such as Typha spp. (cattail) and Schoenoplectus acutus (tule) generate among the greatest annual rates of net primary productivity (NPP, up to 8 kg m2) and soil carbon storage (up to 3 kg m2) for natural ecosystems. Belowground tissues represent 20-80% of total NPP, thus understanding belowground NPP (BNPP) in these wetland ecosystems is particularly important. Under optimal conditions to minimize methane emissions, peat or soil carbon sequestration in restored temperate peatlands may serve an important role in large-scale carbon storage. Remote sensing techniques hold great potential for long-term monitoring of changes in wetland area and carbon stocks, but monitoring belowground carbon pools and productivity remains a challenge given the lack of adequate investigations to build relationships between the aboveground NPP (ANPP) and BNPP. The overarching goal of this proposal is to advance understanding in BNPP and greenhouse gas (GHG) flux measurements of emergent wetland vegetation under varying conditions and scale up the same through advanced remote sensing data and statistical models. The PI has a unique opportunity to collaborate with an interdisciplinary team of USGS researchers who are constructing a farm scale (162 hectare) experimental freshwater marsh wetland on drained peatlands in the San Francisco Bay Delta, CA. The project will test a range of experimental, optimal, and
natural environmental conditions with the goal of optimizing land accretion and net sequestration of GHG through field measurements and eddy covariance with tower measurements. This research proposes three approaches to develop reproducible methods for quantifying and mapping BNPP of emergent wetland vegetation: modeling BNPP from remote sensing of 1) NPP parameters for NPP estimation, 2) aboveground live/dead/dry biomass, and 3) chlorophyll content/plant stress. Field data will be collected over two growing seasons on aboveground live/dead/dry biomass, belowground biomass, leaf area index, light use efficiency (LUE), fAPAR, and chlorophyll concentration, and will be coupled with spectroradiometer readings. Multiple band combinations of simulated hyperspectral, hyperspatial, and multispectral data will be explored through statistical modeling to identify new indices that predict plant biophysical/biochemical features and productivity rates that are related to belowground biomass and carbon sequestration. Satellite data from hyperspectral Hyperion, hyperspatial GeoEye-1/Quickbird and multispectral Landsat ETM+/ALI will be used to map wetland vegetation and scale up the best statistical models to produce maps of belowground biomass and carbon sequestration over a range of pixel resolutions, radiometry, and band-width. Associated uncertainties, errors, and accuracies in carbon sequestration will be highlighted to establish GHG flux levels and experimental treatments that provide the optimal carbon sequestration potential with minimal global warming potential. Research on wetlands in the San Francisco Bay Delta will inspire the development of an intensive field trip on the "Geography of San Francisco Bay Wetlands," to be held at San Francisco's Randall Museum for San Francisco public middle school students who represent underserved groups in STEM. Through hands-on activities, experiments and presentations with NASA imagery, the event will expand awareness and curiosity about the children's local environment and delta environments globally. The Education Plan will also encourage Earth Science projects at the San Francisco middle school science fair and facilitate a tour of the USGS for science fair award recipients. This proposal is relevant to NASA's Earth Science Carbon Cycle and Ecosystems focus area, with its goal to quantify global land cover change and terrestrial productivity, and improve carbon cycle and ecosystem models.